Device, System and Method for Traffic Prediction

ABSTRACT

Some demonstrative embodiments relate to adjusting traffic prediction. Some demonstrative embodiments relate to position related offering of an item.

RELATED APPLICATIONS

The present application claims priority of U.S. Provisional ApplicationNo. 60/267,693, filed Feb. 9, 2001, U.S. Provisional Applications No.60/274,323, filed Mar. 8, 2001 and U.S. Provisional Application No.60/269,083 filed May 7, 2001 which are incorporated by reference.

FIELD OF THE INVENTION

This invention relates generally to a method and system for mappingpotential traffic loads in forward time intervals, according to variouscriteria which might indicate erratic traffic, as a result of expectedincrease in the number of Mobile Telematics Units (MTU) and In-CarNavigation Systems (CNS) users that use Dynamic Route Guidance (DRG). Inparticular, the method and system aims to provide an efficient means toestimate the potential increase or decrease in the number of vehicles inselected places (inconsistent traffic load), by using a radio system, inorder to help in determining levels of a potential erratic behavior inthe traffic due to the use of DRG by a significant percentage ofvehicles. This system and method may further help to investigate sourcesof causes of erratic traffic and their level of effect, including theuse of traffic information and reactions of drivers to telematicsapplications. This could help to improve traffic predictions for the useof traffic control and DRG. In particular, this method provides theability to make use of a mapping system platform which has thecapability to allocate pre-assigned slots or groups of slots for thedetection of signal responses from mobiles that have probe responsecapability. The above identified system is mainly characterized by theability of the mobiles to select time/frequency slots for responsesignals according to a mapping system query and according to apredetermined protocol. The detection of mobile transmission signals ismainly characterized by energy detection of mobile transmitted signalsin allocated slots and hence there is no need for a repeat in mobiletransmission as a result of signal collisions in the same slot. The nonmobile platform of such a mapping system, which may be referred tohereinafter as Slot Oriented Discrimination Mapping System (SODMS), oras otherwise referred to, as well as the mobile (probe) responsecapability are described in U.S. application Ser. Nos. 09/945,257 and09/998,061 filed Nov. 30, 2001 and PCT/IB00/00239 and their ownreferences.

DESCRIPTION OF RELATED ART

For example PCT publication WO 96/14586, published 17 May 1996, thedisclosure of which is incorporated herein by reference, describes,inter alia, a system for mapping of vehicles in congestion. In oneembodiment applicable to the mapping system platform, described in theabove publication, a central station broadcasts a call to the vehicleswhich requests for example those vehicles which are stopped or whichhave an average velocity below a given value to broadcast a signalindicative of their position. Such signals are broadcast in slots, eachof which represent one bit (yes or no) which relates to a position.Preferably, only one logical slot (that may be represented by more thanone actual slot) is used to define the related position. Such signalsare then used to generate a map of those regions for which traffic isdelayed or otherwise moving slowly.

In the above-identified prior art, the possible construction ofconsistent traffic database for possible use with traffic predictionshave been described. Such database could be constructed by trafficmapping of queues, when quasi-stationary (temporary stationary)statistics of traffic flow in a mapped road, at certain periods of timeof a day, and for days in which traffic conditions, are considered to berepetitive. Such collected information, e.g., average arrival rates,could be used as off line database to predict traffic in conjunctionwith real time updates of mapped queues using statistical methods knownin the art. By using the mapping method in this embodiment for mappingthe potential effects of erratic traffic, either when produced as partof the current traffic mapping application of the mapping systemplatform (described by the above identified prior art) or by a separateplatform with similar communication capabilities, it is possible toupdate the consistent traffic database by incorporating inconsistenttraffic predictions.

BACKGROUND TO THE INVENTION

The expected increase in the number of Telematics applications by MTUsused with off-board or on-board route guidance as well as the increasein the number of CNS users would increase the percentage of vehiclesthat would use Dynamic Route Guidance and would hence result inunpredicted changes in traffic load which has the potential to causeerratic traffic.

Traditional traffic predictions could use a database of consistenttraffic in order to predict traffic according to expected traffic loads,possibly also according to prior knowledge about the behavior of thetraffic and the current conditions of traffic. However DRG effects ontraffic might mostly be unpredictable by such a database. This could bethe result even though there is a priori information about off board DRG(routs plans provided by common service centers), since deviations inthe schedule of routes and possible use of alternative routes could in ashort time make prior knowledge to become irrelevant to trafficprediction. Thus it would be valuable to have a means to update atraffic database that would be used in conjunction with consistenttraffic information and possibly with other prior knowledge includingcurrent traffic information in order to improve the capability topredict potential changes in traffic.

Consistent Traffic is defined as such traffic that has a repetitivecharacteristic, with respect to specific time periods and places, (e.g.certain hour in a certain day of the week in a certain road). ConsistentTraffic is a result of behavior patterns that from a statistical pointof view usually and in general may be characterized. Such trafficcharacteristics may be stored in an off-line data base which maycontribute to traffic predictions.

Inconsistent Traffic is defined as such traffic that has a nonrepetitive and erratic characteristic with respect to specific timeperiods and places. Such traffic may for example be the result of theability by the individual driver to change routes according to currenttraffic loads. As the number of drivers that have access to detailedinformation on currently changing traffic increases, and as the numberof drivers that possess in-car sophisticated capability to individuallyvary their previous route plans, and the less coordination if any existsamongst various drivers, the more inconsistent would become suchtraffic. Inconsistent Traffic is difficult if at all possible to becharacterized on a statistical basis. Such traffic tends to be ingeneral unpredictable, and leads to unpredictable traffic loads.

The inconsistent traffic is expected to become a significant issue inthe control of the traffic when a significant percentage of cars will beusing dynamic route guidance and as a result might probably, inthemselves cause unexpected traffic loads at certain places that wouldaffect the traffic and reduce the efficiency of dynamic route guidance.Traffic information used with Dynamic Route Guidance (DRG) could be onereason for the inconsistency in the traffic due to changes in plannedroutes, while driver preferences, deviation from schedule, or reactionto local based services could be other causes for an inconsistency inthe conditions of the traffic.

One general approach to resolve the problem of predicting inconsistenttraffic is to centralize the control of the individual driver routes.This is not the approach which is considered in the following embodimentof the invention as it leads to centralized DRG which has manydisadvantages beside feasibility problems with large scaleimplementation.

As further explained, apart from the contribution of traffic predictionsof inconsistent traffic to traffic control the predictions could furtherlead to a relatively low cost implementation of an anonymous predictiveDRG approach based on distributed intelligence of the in car computersand also to contribute to the implementation of more efficienttelematics applications.

Predictions for inconsistent traffic is based on a process of trafficload estimation for predetermined place and time interval, (for example,estimating the number of vehicles that use in-car navigation computerswhich are expected to pass in a certain road in a certain forward timeinterval). However when the source of such information is limited to carnavigation units that use dynamic route guidance only, and theestimation process is the only means for such predictions, it would berequired that most of the cars should use car navigation systems. Inpractice such a situation would doubtfully be viable. However, thesituation when a significant percentage of vehicular systems would mostprobably be using Dynamic Route Guidance (DRG) may be consideredrealistic in the not too distant future, and hence inconsistent trafficwould begin to appear at an early stage, whereas reliable trafficprediction for this situation would not yet be available. With the lackof traffic predictions, the problems that would be encountered at suchstages could lead to a significant dilemma by the individual drivers,about the efficiency of Dynamic Route Guidance. The dilemma would bewhether to consider recommended DRG according to current traffic, whileignoring unpredictable traffic that might result due to the significantnumber of DRG users, or ignoring the recommended DRG. For such earlystages of inconsistent traffic the following embodiment suggests amodified method of traffic predictions in order to enable reliableprediction at such early stages. Traffic load predictions wouldpreferably refer mostly to sensitive roads that encounter recurrenttraffic jams.

SUMMARY OF THE INVENTION

The present invention provides a preferred method and system fordifferential mapping of potential traffic loads in forward timeintervals in selected places, which could be a result of DRG, in orderto provide rapid and effective means for traffic prediction. The mappingsystem, in which slots are allocated to probe responses, and mobileunits that are equipped with route guidance with probe responsecapability in allocated slots, could be used as a platform for thefollowing modified prediction method. The mobile unit would be referredto as Potential Mobile Mapping System (PMMS). The route guidancecapability of a PMMS could be based on either on board or off boardroute guidance. The prediction method described in the following couldbe implemented with such platforms, either with or without theimplementation of the application of mapping of current traffic as partof this platform. The non mobile part of the mapping system (non mobilesystems), including the radio system and the mapping system, will bereferred to as the non mobile system platform. All applicable terms usedin the above identified prior art, in connection with traffic mapping,and which are applicable and would contribute to the implementation ofthe following embodiment of the invention, will hold also for thisapplication.

The aim of the differential mapping method for determining potentialtraffic loads is to update a traffic information database withinformation about deviation from expected traffic loads in forward timeintervals for selected road segments in order to enable more accurateand prediction capability of the use of a traffic information database.Based on the inherent limitations of the database prediction capability(before deviation updates), prediction criteria are formulated and couldbe transmitted by means of the non mobile platform to the PMMS units.Such criteria are intended to enable the prediction of expectedpotential deviations from schedule and previously planned routes, at thelevel of the database requirements. The PMMS units could determine ifthey match the transmitted criteria, and if a match exists, wouldrespond accordingly. This could also be considered as a method toimprove accuracy levels of information in database that could help topredict traffic according to pre-investigation of local potential loadsaffected by DRG in selected forward time intervals. The level of basicinformation in such database could for example include consistenttraffic, or higher level prediction capabilities.

For example, if the use of the database is based on predictioncapabilities according to consistent traffic, then cars that changetheir planned route according to traffic information, most probably fromthe shortest route according to time and distance to one that mostprobably is shortest according to time, or other dynamic preference,could be used to indicate on possibly expected inconsistent traffic thatis not taken into account within consistent traffic statistics. Thus itwould be worth to first isolate this group of cars in order to estimatetheir contribution to the inconsistent traffic loads in specific roadsegments. Preferably, this information would then be taken into accountin conjunction with a database of consistent traffic statistics,preferably updated with current real time updates of traffic, todetermine current and predicted traffic information that would becurrently updated accordingly. The isolation process would useprediction queries that would selectively target cars that made a changeto their route or deviated from schedule, according to trafficinformation or other predetermined possible reasons such as a responseof drivers to a telematics application. The queries determine theresponse criteria which will include but not be limited to thefollowing—a) vehicles that are planning to pass in a certain road at acertain forward time interval according to their modified route plan orschedule, and which did not plan to do so according to a reference route(e.g., a default route or any other route that could be referred by thePMMS as a reference that may be determined according to criteria as partof a predetermined protocol), and b) vehicles that did plan to pass inthis road according to the reference route, and are not planning to doso according to the modified route plan or schedule, at the aboveforward time interval.

Vehicles which are using their reference (e.g. default) route will notrespond to queries.

Criteria for determining whether a route is within reference conditions(e.g., default) or not, could be provided from a common external source,which considers the investigated level of possible effect on the trafficstatistics. The reference (e.g., default) route information may beformed either in the in-car (on board) systems, or received fromexternal (off board) sources, and would preferably be determined byroute plan and schedule. Thus, according to a predetermined protocol, adeviation in route or schedule would exclude the route from beingreferred to as a reference route and would determine it to be a nonreference route. The protocol would preferably include threshold levelsof deviation.

Typical default routes are such which could be considered but notlimited to conform with consistent traffic. Default routes could bedetermined according to common criteria (e.g. the shortest route,preferably with time schedules), for mobile units participating in thefollowing processes. Non default routes are such that have somesignificant effect on known traffic statistics as a result of deviationfrom schedule or from original route plan that could be considered asdefault routes.

The in-car system will incorporate a predetermined decision procedure,described in the following.

In principle, a Differential Traffic Load Prediction (DTLP) process withrespect to a Forward Time Interval related Route Segment (FTIRS refersto a time interval with respect to a route segment, usually a roadsegment) under investigation, could be implemented by means of two typesof traffic prediction queries which would be transmitted by a mappingsystem to the PMMS units. The prediction queries include the predictioncriteria, and are aimed at targeting groups of cars that are eitherexpected to pass through the FTIRS under investigation and were notexpected to do so, according to database information, (non expectedvehicles—NEV), or are not expected to pass through the FTIRS underinvestigation, and were expected to do so, according to the databaseinformation (expected vehicles—EV);—

Query—A):—type of a query with the aim of estimating the number ofvehicles which on their reference route are not expected to pass throughthe investigated FTIRS, and on their non reference route are expected topass through the investigated FTIRS, (non expected vehicles—NEV), andQuery—B):—type of a query with the aim of estimating the number ofvehicles which on their reference route are expected to pass through theinvestigated FTIRS and on their non reference route are not expected topass through the investigated FTIRS, (expected vehicles—EV).

In order to enable responses in relation to forward time intervals, itis required that the PMMS units would be equipped with the means ofreference or mean to calculate reference to segments of planned routesand estimated travel time intervals along respective route segments.Preferably, an estimated time interval will be provided with respectiveconfidence intervals.

Vehicles which are using a non reference planned route, will enable theresponse procedure according to the following decision procedure;

If the received query is identified as Query A, then, according to thefollowing differential traffic load match process result, if there is amatch between FTIRS in the query and the planned non reference (e.g.,default) route (route in use), and there is no match between FTIRS inthe query and the reference route, then enable the response procedure.

If the received query is identified as Query B, then, according to thefollowing differential traffic load match process result, if there is amatch between FTIRS in the query and their reference route, and there isno match between the FTIRS in the query and non reference route (routein use), then enable the response procedure.

Enabling the response procedure, in the predetermined decisionprocedure, would preferably be expanded to include additional criteria,for targeting vehicles. For example, with respect to Query A, additionalcriteria in checking an interval estimate for the probability to arrivewithin the investigated FTIRS, would preferably be taken into account aspart of the decision procedure.

In order to alleviate the computation load in the in-car system,involved in frequent matching in response to above queries, it would bepreferable to refer routes to predetermined area zones, and by apreliminary predetermined screening procedure, preceding the abovedecision procedure, vehicles whose planned (reference and non reference)routes do not cross area zones in which the FTIRS is included, will notcontinue with the more detailed matching process in the above decisionprocedure.

A number of communication slots will be preferably allocated forresponders (cars which transmit in the allocated slots) in the responseprocedure, separately, with respect to each Query. Each of the targetedvehicles, (responders), in which the response procedure is enabled, willuse a predetermined response procedure to select a slot in which torespond. This predetermined procedure would preferably use a uniformlydistributed random selection of a slot out of all the allocated slots,to transmit a signal.

In accordance with an embodiment of the invention, there is thusprovided a method of predicting load of traffic of vehicles that aretraveling according to non reference route plan, provided with DynamicRoute Guidance capability of their PMMS, in a Forward Time Intervalrelated Route Segment and according to a predetermined protocol betweenmobile systems and a non mobile system platform of a SODMS, the methodcomprising:

(a) receiving by mobile units a traffic prediction query and accordingto a predetermined differential traffic load match process,(b) performing a match process by each of the mobile units and,according to a match,(c) enabling a predetermined response procedure wherein a responseprocedure in each mobile unit uses a predetermined random process toselect an allocated slot in which to transmit a predetermined signal,which provides an improved way to predict traffic in conjunction withoff line database statistics, preferably with such that are beingadaptively corrected by prior data and method to predict traffic whichdo not include, or lack sufficient erratic traffic information.

In another embodiment of the invention it would be valuable to usetraffic predictions in conjunction with applications which have apotential to cause erratic conditions of traffic. Such applicationscould include local based services in telematics and in particularposition related commerce (p-commerce sometimes referred to asl-commerce or m-commerce). There might be different ways to implementp-commerce and hence to increase the level of unpredicted traffic. Forexample in order to improve p-commerce applications, it would be anadvantage to large stock holders and others to have a query tool thatwould help them to identify sufficient demand, preferably according toprices and including non solicited products, for special offers. Thiscould create a hunting trip environment. With such a tool, queries couldbe provided in a way similar to an auction process, preferably by abroadcast message to the telematics users, with respect to products withpossibly one or more ranges of prices. The user, usually a driver, willhave a stored list of preferences for products, in his TelematicsComputer (TC which could be the computer of a Telematics-PMMS) thatwould be matched with broadcast messages according to preferences in thelist. For example, a stored product list (SPL) which may includeproducts with ranges of prices could enable the TC to respond to abroadcast query. If such responses would provide information about theestimated number of the potential clients and possibly their positiondistribution it would enable the vendor to determine a time window andprice for a special offer according to demand. The offer could thentarget the potential clients. Most probably this would target theresponders who would contribute to the decision making. When consideringa system platform with capabilities such as suggested for a trafficmapping system, both, in this embodiments of the invention and in thereference prior art, together with telematics mobile unit with PMMScapabilities, which enable to estimate the number of responders to aquery by random response in predetermined number of slots, it would bepossible to implement a hunting trip application, efficiently.

Thus in accordance with this embodiment of the invention, there is thusprovided a method for estimating according to criteria and apredetermined protocol local demand (e.g., for products or services)according to SPL, preferably in conjunction with predicting respectiveload of differential traffic in forward time intervals for selectedplaces which might result from a hunting trip application, and accordingto a further predetermined protocol between TCs and a non mobile systemplatform of a SODMS, the method comprising:

(a) receiving by TC units a query of a hunting trip application andaccording to a predetermined match process,(b) performing a match process by each of the TC units and, according toa match,(c) enabling a predetermined response procedure wherein a responseprocedure in each TC unit uses a predetermined random process to selectan allocated slot in which to transmit a predetermined signal.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1, describes an iterative estimation procedure that is preferablyused with more than a single iteration of estimation (separateallocation of slots with each iteration). The iterative estimationprocedure is preferably aimed to obtain an estimated result of thenumber of responders with a restricted acceptable error level and toreduce biasness. The error level of the estimate in a single iterationis a function of the ratio between the number of slots in whichresponses are detected (responding slots) and the given number ofallocated slots. Since the ratio of responding slots to a given numberof allocated slots would be a result of the number of responders, it isdesirable to assess in advance a realistic anticipated range ofresponders, in order to determine a minimal number of initial allocatedslots.

DETAILED DESCRIPTION OF THE DRAWINGS

FIG. 1, describes an iterative estimation procedure that is preferablyused with more than a single iteration of estimation (separateallocation of slots provided with each performed iteration). Theiterative estimation procedure is preferably aimed to obtain anestimated result of the number of responders with a restrictedacceptable error level, to reduce biasness and to check consistency. Theerror level of the estimate in a single iteration is a function of theratio between the number of slots in which responses are detected(responding slots) and the given number of allocated slots. Since theratio of responding slots to a given number of allocated slots would bea result of the number of responders, it is desirable to assess inadvance a realistic anticipated range of responders, in order todetermine a minimal number of initial allocated slots. Since suchrealistic ranges of responders could be anticipated from statisticaldata, according to time and place, then a data base of possible initialranges would preferably be evolved for any particular urban entity,preferably as probability distribution from which ranges of confidenceintervals could be derived. Combined estimates that can use jointprobabilities and Bayesian methods as described above with respect toFIG. 1 are described in more detail in the detailed description ofPreferred Embodiment of the invention.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

The present invention provides a preferred method and system fordifferential mapping of potential traffic loads in forward timeintervals in selected places, which could be a result of DRG, in orderto provide rapid and effective means for traffic prediction. The mappingsystem, in which slots are allocated to probe responses, and mobileunits that are equipped with route guidance with probe responsecapability in allocated slots, could be used as a platform for thefollowing modified prediction method. The mobile unit would be referredto as Potential Mobile Mapping System (PMMS). The route guidancecapability of a PMMS could be based on either on board or off boardroute guidance. The prediction method described in the following couldbe implemented with such platforms, either with or without theimplementation of the application of mapping of current traffic as partof this platform. The non mobile part of the mapping system (non mobilesystems), including the radio system and the mapping system, will bereferred to as the non mobile system platform. All applicable terms usedin the above identified prior art, in connection with traffic mapping,and which are applicable and would contribute to the implementation ofthe following embodiment of the invention, will hold also for thisapplication.

The aim of the differential mapping method for determining potentialtraffic loads is to update a traffic information database withinformation about deviation from expected traffic loads in forward timeintervals for selected road segments in order to enable more accurateand prediction capability of the use of a traffic information database.Based on the inherent limitations of the database prediction capability(before deviation updates), prediction criteria are formulated and couldbe transmitted by means of the non mobile platform to the PMMS units.Such criteria are intended to enable the prediction of expectedpotential deviations from schedule and previously planned routes, at thelevel of the database requirements. The PMMS units could determine ifthey match the transmitted criteria, and if a match exists, wouldrespond accordingly. This could also be considered as a method toimprove accuracy levels of information in database that could help topredict traffic according to pre-investigation of local potential loadsaffected by DRG in selected forward time intervals. The level of basicinformation in such database could for example include consistenttraffic, or higher level prediction capabilities.

For example, if the use of the database is based on predictioncapabilities according to consistent traffic, then cars that changetheir planned route according to traffic information, most probably fromthe shortest route according to time and distance to one that mostprobably is shortest according to time, or other dynamic preference,could be used to indicate on possibly expected inconsistent traffic thatis not taken into account within consistent traffic statistics. Thus itwould be worth to first isolate this group of cars in order to estimatetheir contribution to the inconsistent traffic loads in specific roadsegments. Preferably, this information would then be taken into accountin conjunction with a database of consistent traffic statistics,preferably updated with current real time updates of traffic, todetermine current and predicted traffic information that would becurrently updated accordingly. The isolation process would useprediction queries that would selectively target cars that made a changeto their route or deviated from schedule, according to trafficinformation or other predetermined possible reasons such as a responseof drivers to a telematics application. The queries determine theresponse criteria which will include but not be limited to thefollowing—a) vehicles that are planning to pass in a certain road at acertain forward time interval according to their modified route plan orschedule, and which did not plan to do so according to a reference route(e.g., a default route or any other route that could be referred by thePMMS as a reference that may be determined according to criteria as partof a predetermined protocol), and b) vehicles that did plan to pass inthis road according to the reference route, and are not planning to doso according to the modified route plan or schedule, at the aboveforward time interval.

Vehicles which are using their reference (e.g. default) route will notrespond to queries.

Criteria for determining whether a route is within reference conditions(e.g., default) or not, could be provided from a common external source,which considers the investigated level of possible effect on the trafficstatistics. The reference (e.g., default) route information may beformed either in the in-car (on board) systems, or received fromexternal (off board) sources, and would preferably be determined byroute plan and schedule. Thus, according to a predetermined protocol, adeviation in route or schedule would exclude the route from beingreferred to as a reference route and would determine it to be a nonreference route. The protocol would preferably include threshold levelsof deviation.

Typical default routes are such which could be considered but notlimited to conform with consistent traffic. Default routes could bedetermined according to common criteria (e.g. the shortest route,preferably with time schedules), for mobile units participating in thefollowing processes. Non default routes are such that have somesignificant effect on known traffic statistics as a result of deviationfrom schedule or from original route plan that could be considered asdefault routes.

The in-car system will incorporate a predetermined decision procedure,described in the following.

In principle, a Differential Traffic Load Prediction (DTLP) process withrespect to a Forward Time Interval related Route Segment (FTIRS refersto a time interval with respect to a route segment, usually a roadsegment) under investigation, could be implemented by means of two typesof traffic prediction queries which would be transmitted by a mappingsystem to the PMMS units. The prediction queries include the predictioncriteria, and are aimed at targeting groups of cars that are eitherexpected to pass through the FTIRS under investigation and were notexpected to do so, according to database information, (non expectedvehicles—NEV), or are not expected to pass through the FTIRS underinvestigation, and were expected to do so, according to the databaseinformation (expected vehicles—EV);—

Query—A):—type of a query with the aim of estimating the number ofvehicles which on their reference route are not expected to pass throughthe investigated FTIRS, and on their non reference route are expected topass through the investigated FTIRS, (non expected vehicles—NEV), andQuery—B):—type of a query with the aim of estimating the number ofvehicles which on their reference route are expected to pass through theinvestigated FTIRS and on their non reference route are not expected topass through the investigated FTIRS, (expected vehicles—EV).

In order to enable responses in relation to forward time intervals, itis required that the PMMS units would be equipped with the means ofreference or mean to calculate reference to segments of planned routesand estimated travel time intervals along respective route segments.Preferably, an estimated time interval will be provided with respectiveconfidence intervals.

Vehicles which are using a non reference planned route, will enable theresponse procedure according to the following decision procedure;

If the received query is identified as Query A, then, according to thefollowing differential traffic load match process result, if there is amatch between FTIRS in the query and the planned non reference (e.g.,default) route (route in use), and there is no match between FTIRS inthe query and the reference route, then enable the response procedure.

If the received query is identified as Query B, then, according to thefollowing differential traffic load match process result, if there is amatch between FTIRS in the query and their reference route, and there isno match between the FTIRS in the query and non reference route (routein use), then enable the response procedure.

Enabling the response procedure, in the predetermined decisionprocedure, would preferably be expanded to include additional criteria,for targeting vehicles. For example, with respect to Query A, additionalcriteria in checking an interval estimate for the probability to arrivewithin the investigated FTIRS, would preferably be taken into account aspart of the decision procedure.

In order to alleviate the computation load in the in-car system,involved in frequent matching in response to above queries, it would bepreferable to refer routes to predetermined area zones, and by apreliminary predetermined screening procedure, preceding the abovedecision procedure, vehicles whose planned (reference and non reference)routes do not cross area zones in which the FTIRS is included, will notcontinue with the more detailed matching process in the above decisionprocedure.

A number of communication slots will be preferably allocated forresponders (cars which transmit in the allocated slots) in the responseprocedure, separately, with respect to each Query. Each of the targetedvehicles, (responders), in which the response procedure is enabled, willuse a predetermined response procedure to select a slot in which torespond. This predetermined procedure would preferably use a uniformlydistributed random selection of a slot out of all the allocated slots,to transmit a signal.

A predetermined estimating procedure will be used in the non mobilesystem platform, to determine estimated number of responders accordingto the total number of slots in which responses are detected in a givennumber of allocated slots. The estimating procedure would preferably usea number of secondary procedures, as described in the following andillustrated in FIG. 1. It is preferably aimed to obtain the estimatednumber of responders with an acceptable error level, however the errorlevel is a function of the ratio between the number of responders andthe given number of allocated slots. The greater the number of allocatedslots in proportion to the number of responders, the lower would be theerror level. The error level can be defined as the maximum cumulativeprobability that could produce a similar result from a number ofresponders which is either greater or lower than the acceptableestimation interval of responders. The acceptable error level wouldpreferably be determined according to the sensitivity of the estimationin the specific application. Since there is a variation around the mostfrequent number of responding slots, (slots in which responses aredetected), which depends on the number of allocated slots and the numberof responders, it is desirable to assess in advance a realisticanticipated range of numbers of responders, in order to determine aminimal number of initial allocated slots for an acceptable variance.Since such realistic ranges of responders could be anticipated fromstatistical data, according to time and place, then a database ofpossible initial ranges would preferably be evolved for any particularurban entity, (preferably as probability distribution from which rangesof confidence intervals could be derived). The database of ranges wouldbe preferably evolved taking into account conditions specific to such anentity, such as, (but not limited to), characteristic trafficconditions, characteristic infrastructure servicing traffic flow, andprevailing decision processes used by route guidance procedures. Thetechnique of evolving a database of ranges for initial numbers ofexpected responders would preferably be based on statistical andempirical methods and computer simulations. In order to determine therequired initial number of allocated slots, based on the database ofranges, it is also preferably required to take into account theprevailing conditions in available radio communication spectrum,limitations imposed by the need to investigate preferred number of FTIRSin a reasonably meaningful short cycle time, and an acceptable tolerableerror in the resulting predictions. Since the initial determined numberof allocated slots might not achieve the preferably acceptable errorlevel, successive repetitive iterations in allocation of slots andre-estimation of number of responders, might be required. In order todetermine the possible need for adjustment of number of allocated slotsin a minimal number of iterations, an error estimating function, and anoptimized adjustment function, would preferably be evolved. The errorestimating function would preferably estimate the error, (e.g., byconfidence interval) in the resulting estimated number of responders, asa function of the ratio between the number of detected number ofresponding slots (responses) and number of allocated slots (preferablyconsidering the probability distribution of responders). Based on theerror estimating function, the required preferred number of allocatedslots may have to be adjusted for a further iteration, and may also varyduring a possible series of iterations. The optimized adjustment processin arriving at the preferred number of allocated slots with a minimalnumber of iterations would preferably use earlier results (with a nonacceptable tolerable error), to predict according to statisticalcombination the required improvement in the error level (e.g., computingMaximum Likelihood Estimates or Estimates), and to determine accordinglythe preferred required number of allocated slots to be used in thesubsequent iteration, in order to save further iterations. Thesignificance in performing iterations is, in addition to the potentialin reducing the error level, in checking consistency, particularly incases where little, or no, a-priori knowledge exists about theprobability distribution of responders that provide a certain number ofresponses. Thus, at least two iterations would preferably be allowedeven though the first proportion between the number of responses andallocated slots might be satisfying, i.e., indicating on an acceptableerror level.

The estimating procedure would preferably use statistical methods whichcould produce acceptable estimation intervals (based on intervalestimation approach such as confidence and tolerance intervals withupper and lower limits). A single point that is the most frequent numberof responses (responding slots) in a pre-determined number of slots forpre-determined simulated (or analytically calculated) number ofresponders could provide the distribution of the number of responsesaround this point and could determine a tolerance interval for theinterval estimate. The most frequent number of responses will bereferred to in the following as single point estimate for the number ofresponders in a predetermined number of slots. One conservative way ofdetermining an acceptable estimation interval for decision making aboutthe possible range of responders that respond by a certain number ofresponses in a predetermined number of allocated slots, is by firstdetermining a tolerance interval according to a respective single pointestimate, either produced by a simulation of responses according to acertain repeated number of responders in certain number of allocatedslots or by analytical calculation, then, to determine according to theresponse distribution of the responses an acceptable tolerance interval.Based on the acceptable tolerance interval it is enabled to determine,either by simulation or by analytical calculation, two other responsedistributions for the same number of allocated slots which indicate onthe potential of an upper and lower number of responders to produceresponses within the acceptable tolerance interval, by determiningacceptable error e.g., according to cumulative-probability of theoverlap (analogous to error type II in hypothesis testing, with respectto an acceptance region). As a result of the single point estimates ofthe upper and lower distributions of responses which overlap with thetolerance interval within an acceptable error it would be enabled todetermine upper and lower numbers of responders which could be used tofurther determine upper and lower limits to an acceptable interval forthe estimation of potential responders that might produce the samenumber of responses in the allocated slots. The upper and lower limitsof this interval could be determined with respect to the sensitivity ofthe decisions that have to be taken accordingly. Such limits could alsobe interpreted as determining the rejected regions of potentialresponders. From the point of view of the acceptable estimation intervaldefinition, for a significantly wide range of different numbers ofresponses for a sufficient number of slots, consistency in terms ofpercentage of error would be expected around said single point estimatesfor a respective range of responders due to close to linear relationbetween said single point estimates and respective responders in thatrange. An alternative approach to determine estimation intervals is byproducing probability distribution function (PDF) of potentialresponders around a said single point estimate, either analytically orby simulation, from which the acceptable estimation interval could bederived e.g., according to the confidence interval of this PDF. Such aPDF could be used for traffic behavior analysis according to differentcriteria, e.g., criteria which characterize reaction of mobile units totelematics applications, which may cause erratic traffic. Each PDF couldbe derived for a certain number of allocated slots by normalizingsimulated distributions of the relative frequency of a certain number ofresponses, determined by a said single point estimate related to acertain number of responders, which may be produced with other (lower)relative frequency by responders which have a different number from thenumber of responders which relates to the said point estimate. Asufficiently high range of the number of responders should be used toenable the normalization of the relative frequencies of the responses todetermine a said PDF. For high accuracy of the relative frequencies thatshould be determined also for high number of potential responders(theoretically unlimited but practically limited by the application) asufficiently high number of repetitions of response procedures should beused, to determine the relative number of the responses, for the saidnumber of responses determined by the said single point estimate ofresponders (tested according to a number of allocated slots). Repeatingthe simulation for a sufficient range of numbers of responders toprovide relative frequencies of the same number of responses aroundrelative frequency derived according to the said single point estimatewould determine a distribution of the said number of responses accordingto the (practical) range of numbers of the potential responders.According to the accumulated number of responses that produce therelative frequencies of responses (according to the said sufficientlyhigh number of repetitions to the same number of responders) anormalization phase can be taken to produce a said PDF. The simulationcould be further expanded to determine such distributions for differentnumbers of allocated slots around different numbers of responders(determined by said single point estimate). Such PDFs could be used toprovide confidence intervals for single estimate of responders withsingle allocation of slots. For estimates that would use more than asingle allocation of slots it would be valuable to create joint PDFs forcombinations between different numbers of slots with different numbersof responders related to the said single point estimates. Errorestimating functions could further be formulated according tostatistical methods and by simulations that could consider a-prioriknowledge about the probability distribution of responders (Bayesianapproach). The estimating process would count the number of the slotsthat were detected to be used by at least one responder and will usethis number as an input to a predetermined estimating function (e.g.,based on pre stored table that includes PDFs, confidence intervals, andupper and lower limits of said acceptable estimation intervals,constructed according to simulations) which could provide requiredestimates as a function of number of slots detected to be used byresponders in the allocated slots. The estimate would be considered asthe estimation of the number of vehicles according to the querycriteria. Estimating functions (tables) could be predeterminedpreferably by using the described method for simulation and otherstatistical methods known in the art. Separate estimating functionswould be preferably evolved for various ranges of numbers of allocatedslots. An increase in the number of allocated slots ought to shorten theacceptable estimation interval. In practice this would enable to usemore efficiently the allocated communication resources. Response anddetection procedures could further include a possible discriminationbetween number of responders in each slot. However this would requireaccurate power control on the transmitters of the responders which forshort burst transmissions could be more costly to be implemented (e.g.,CDMA). Non information signals would be preferably used by theresponders. However, if information bearing signals are used by theresponders capture effects also could be considered to distinguishbetween slots. Nevertheless short energy burst in slots could minimizetime of detection and hence preferably fit to the response procedurewhere responders use allocated slots randomly by the responders and thedetection process of their transmitted signals could consider justenergy detection.

The estimations that may according to one type of query selectivelyrepresent additional number of vehicles that were not expected(preferably according to probabilistic levels) to arrive to the FTIRS,(NEV), and according to a different type of query, the number ofvehicles that were expected (preferably according to probabilisticlevels) to arrive to the FTIRS and would not arrive to the FTIRS, (EV),would indicate on change in expected load, in the FTIRS. This could beused in conjunction with an off line database of traffic statistics todetermine according to the expected traffic and the non expected traffic(predicted differential traffic load) the weighted sum of the missingEVs and the additional NEVs with the predictable traffic load in thesegment of road (e.g., by using statistical methods known in the artsuch as convolution between PDF of the estimate of the expected load inthe database and the estimated number of NEVs, would provide a PDF ofthe updated estimate to be used for the computation of a new expectedload due to NEVs).

For this purpose it would be useful to construct respective PDF's inconjunction with the function tables that are produced to provideestimation intervals, as further described in the detailed description.

This is the basis for an improved way to predict traffic in conjunctionwith off line database statistics, preferably with such that are beingadaptively corrected by mapping of the current traffic.

In addition to the contribution potential of such improvement to centralcontrol on traffic it would have the potential to improve, and evenenable, reliable dynamic route guidance. However the way of how to usesuch predictions is a very important issue when considering theextensive use of car navigation systems, in which the planned routes arebeing independently modified according to such predictions. Thefollowing highlights a preferable method by which such predictions couldenable efficient distributed DRG.

In order to explain the benefit of this approach for implementingdistributed DRG it would be worth to describe traditional approaches incomparison.

In order to overcome unpredictable traffic problems, in the future,traditional approaches are considering a system that would be almostfully controlled, i.e., in-car computers will not make the decisions fortheir best route but rather a Big Brother approach will do it byproviding the recommended routes in order to maintain predictivetraffic. This approach would use a central computation method that willhave to maintain the knowledge on the destination of each vehicle aswell as its current position along the road. Beside the numerouscomputations that it would require it would need a communicationplatform that would have to accommodate a huge volume of data that willconnect the vehicles to the control center. In practice, roadsidebeacons that have two way communication capabilities are considered forthis purpose. Apart from the non privacy characteristic of such a systemit will have a tremendous cost and will require computation power thatprobably makes the idea impractical for wide coverage implementation.This problem increases when a significant number of drivers would notobey the central route guidance, and hence it will reduce the systemefficiency and could even make it unreliable. For such reasons a conceptof predictive Dynamic Route Guidance based on distributed intelligenceshould preferably be used whereby in-car computers would be makingdecisions on their preferred routes. However, with such an approach thetraffic would probably become even more unpredictable. To overcome thisproblem there would be a need to cope with unpredicted traffic in a waysuch as proposed above and to use periodical corrections to statisticaltraffic databases. To realize such an approach, predicted trafficinformation would have to be, periodically, estimated and then providedto the car navigation computers so that a trial and fail based processwould be used to refine an equilibrium between the individual needs andthe offered traffic routes. This would implement a system based ondistributed intelligence in which, in addition to taking into accountcurrent traffic information, the car navigation computers would have touse a predetermined give-up process which, according to the predictedtraffic information and their planned route, each car would try toidentify if its planned route is going to take part in a predictedtraffic congestion or traffic jam. The identification of such situationwould result from a comparison between the predicted traffic informationand the planned route. If the comparison would identify predictedtraffic congestion along the planned route it would automatically giveup on its planned route, if it would have a more reasonable alternativeroute. The give up process would preferably be used according topriorities and could consider various criteria levels. For example, in afirst iteration of such trial and fail cycle, cars that would have analternative route that might increase the length of their planned routeby, say 5 percent, but would not significantly affect their travelingtime, would automatically change their planned route to the alternativeroute which a-priori had a lower priority. A further cycle of predictionand update to the cars, probably indicating on changes in trafficpredictions according to the reactions of cars to the previous give upprocedure cycle, could either result in additional cars, with a highergrade of give up level (e.g., alternative route with say 10% increase inlength to remainder of planned route), to give up on the planned route,if previously predicted traffic congestion still predicted. Suchprocedures might, some times, allow cars to return to an earlier, morepreferable, route (reduced grade of give up level), in the case that toomany cars have given up on their planned routes at a previous iteration,and accordingly traffic loads are alleviated. In addition topredetermined give up process based on parameters of increase andreduction of give up levels, random parameters might preferably be usedin order to refine, and even to control the convergence iterativeprocess. As a result of a sufficient number of such iterations, thisprocess could lead to a convergence to equilibrium, with the grade ofgive up level and its reduction tapering off. Trade off between low andhigh levels of give up grades would preferably be taken into account,with the parameters of the iterative process.

When Car Navigation System (CNS) with on board DRG capability areconsidered as being used it would be easy to observe the benefit of suchapproach since periodical process of such prediction processes couldhelp to refine the preferred route by on board DRG of the CNS units.However one of the trends in telematics is to provide off board DRG toTelematics Computers (TC) installed in cars. Such TC would be providedwith a recommended route and according to in-car positioning means theTC could navigate the driver along the route. Thus to enable handlingthe traffic predictions in an environment that partially use TC with offboard DRG and another part uses CNS units with on board DRG it would benecessary to provide enhanced capability to TC units. For example, a TCwill be provided with a few alternative routes, (e.g., bypass segmentsof routes), in order to overcome possible traffic load problems inpredetermined segments investigated in the prediction process. Thesealternatives, would be used, according to priorities by the TC, thatwould be equipped with a radio interface, such as used with the CNShaving on board DRG, enabling it to participate in prediction processes.Thus, by participating in the prediction processes the route plan wouldbe refined by using a give up procedure, according to a balance betweencurrent and predicted traffic.

The predicted information would be preferably provided through abroadcast channel, e.g., RDS/TMC, to car navigation end users and offboard DRG service providers as well as to traffic control centers.

Another embodiment of the implementation the differential trafficprediction process deals with effects on traffic loads as a result oftelematics applications, such as Local Based Services. One type of suchtelematics application is position related commerce service, sometimesnamed as p-commerce, m-commerce or I-commerce. With such a serviceapplication, a service user would preferably initiate a request tolocate points of interest according to criteria. For example a requestmay ask for locations where a certain product may be found, withpossible restrictions to some range of prices and possibly within acertain distance from the user's position. Another application oftelematics is more advertisement oriented and could be initiated by avendor who wishes to provide ordinary or special offers to driverspossibly for a short term. In order to enable the vendor to administersuch offers efficiently it would be valuable to have a priori knowledgeabout the potential demand for an offer. One way to get such informationis to use recorded information of requests initiated by the potentialbuyers to assess the demand potential for a certain level(s) of price. Aproblem, involved with special offers, could be the lack by vendors of apriori knowledge about potential buyers who might otherwise showinterest in many different products, other than those, subject to aspecial offer.

Beside the effect of p-commerce on the traffic load there might bedifferent ways to implement p-commerce and hence to increase the levelof unpredicted traffic. For example in order to improve p-commerceapplications, it would be an advantage to large stock holders and othersto have a query tool that would help them to identify sufficient demand,preferably according to prices and including non solicited products, forspecial offers. This could create a hunting trip environment. With sucha tool, queries could be provided in a way similar to an auctionprocess, preferably by a broadcast message to the telematics users, withrespect to products with possibly one or more ranges of prices. Theuser, usually a driver, will have a stored list of preferences forproducts, in his Telematics Computer (TC) that would be matched withbroadcast messages according to preferences in the list. For example, astored product list (SPL) which may include products with ranges ofprices could enable the TC to respond to a broadcast query. If suchresponses would provide information about the estimated number of thepotential clients and possibly their position distribution it wouldenable the vendor to determine a time window and price for a specialoffer according to demand. The offer could then either target thepotential clients and possibly others. Most probably this would targetthe responders who would contribute to the decision making. Whenconsidering a system platform with capabilities such as suggested for atraffic mapping system and telematics mobile unit with PMMScapabilities, (that uses pre assigned slots to determine position andother distributions of responders according to queries, and possibly toestimate the number of responders to a query by random response inpredetermined number of slots), it would be possible to implement ahunting trip application, efficiently.

A possible scenario could start with an update of one or more productsin the SPL (in the TC) according to predetermined criteria (for examplea product name and range of prices of interest). A driver who enablesthe hunting trip application of the TC would enable the TC to listen tobroadcast queries and to participate in responses to such queries.Queries would be matched with the SPL and would enable a response of theTC to an identified match. If the query is a distribution related querythen according to a predetermined protocol the TC would initiate aresponse in a communication slot which best indicates on its attributeaccording to a characteristic value. For example, for a query whichinvestigates distribution of potential clients in a restricted area, anddetermines responses to be activated in predetermined slots, it wouldrespond in a slot that would best indicate on its position, in a rangedetermined by the slot. In this case the characteristic valuecorresponds directly to position. Another possibility could be the useof a characteristic value that corresponds to estimate of time ofarrival, which would require calculated travel time, in which case thequery would possibly relate to time of arrival distribution, rather thanuser position. Another possibility could estimate statistically thenumber of potential clients by responding, according to a predeterminedprotocol, randomly in determined number of slots which could provide,according to the proportion between the number of slots that were usedby the responders and the number of the allocated slots to responses, anestimate to the number of the potential of respectively hooked vehicles(such an estimation could use the interval estimate approach describedwith the differential traffic load prediction method of estimatingtraffic loads in FTIRS). An assessment of the demand could help thevendor to determine whether to make an offer and for what price. Abovemethods may be used independently or in combination with each other inorder to enable a vendor to make a decision about presenting an offer.

Implementing an offer could possibly use a broadcast message, whichwould refer to a specific previous investigation query, and uservehicles which had previously responded to this query, would be targetedby their matching with the record in the TC of the response to thequery, and which was stored according to the predetermined protocol. Thetargeted user could then be invited to respond by manual interventionand possibly confirm his wish to accept the offer. At this stage thevendor could possibly initiate an additional second broadcast querytargeted to the users that accepted the offer, according to the recordstored in the TC, with respect to the specific message, in order tofinally assess the demand. The user vehicles in which there is a matchbetween the second broadcast query and the stored record in the TC wouldrespond in slots according to the predetermined protocol with respect tothis query. The vendor could then confirm the offer, by implementing abroadcast message to the responders to the second query. At this stageit would be a preferable possibility to enable a registration process,in order to ensure purchase. Any communication method used with the TCmay be used for this purpose. However such processes and othertelematics applications have the potential to create unpredictabletraffic due to changes in planned routes. Thus a further process thatwould involve estimates of deviations in traffic loads as a result ofsuch processes could be used. For example TC units which each could bepart of a PMMS (Telematics PMMS—TPPMS) that made a change to route plansaccording to a telematics application such as a hunting trip could betargeted by traffic prediction queries by criteria that include recentchange to the route plan according the telematics application.Implementation of such traffic predictions would a) help investigate theinfluences of such telematics applications upon traffic, and b) enablepossible processes of control of such influences, for example, bycontrolling the scope of the offers, so as to obviate resulting trafficcongestions. (In hunting trip applications this might take the form oflimiting the scope of offers to a given acceptable range, or to limitpotential arrivals from certain directions or through certain roadsegments).

The invention has been described herein using examples in which theindication signals transmitted by the responders in the allocated(transmission) slots are transmitted in time, frequency or time andfrequency slots, preferably as RF (radio frequency) pulse. Other typesof transmission slots are also useful in the invention such as frequencyhopping and other spread-spectrum transmission slots. The term“transmission slots” or “slots” as used herein includes all these typesof slots.

In a case when there would be possibly a need to further map trafficqueues in the local area in order to complement or improve the level ofconsistent type traffic information, possibly as a result of the need touse in conjunction with the need to map erratic traffic as a result oflocal based telematics services, such as mentioned in an embodimentabove. One method proposed by above identified prior art was to maptraffic queues. In this respect a further embodiment, provided by thefollowing, could improve the radio communication efficiency for queuemapping for a slot oriented discrimination mapping system (SODMS)described in the above identified prior art.

When assigned slots are allocated to construct a mapping sampleaccording to a distance from a mapping focus, there is a way whichenables to save the number of allocated slots by considering that in anysubsequent mapping sample, in mapping a queue of vehicles, it is justrequired to check if a new probe, arriving to the queue after a previousmapping sample, is farther from the mapping focus than the farthestprobe in a previous mapping sample. Thus, in a preferable implementationprocess of sampling, the assignment of allocated slots in a mappingsample that is taken subsequently, (to a mapping sample in which thefarthest probe was detected), can be limited for a segment in the roadthat starts at a position which was identified as the position of thefarthest probe (from the mapping focus) in a previous mapping sample.The subsequent sample would cover the mapped range in a directionfarther from the mapping focus, for a length which may preferably bedetermined from statistical data. Additional slots may preferably beallocated exclusively to the farthest identified probe in a mappingsample, in order to determine the motion rate in a queue according tothe motion distance of the farthest probe in between successive mappingsamples. These slots could be used by such probe for transmission ofdata in any one of two ways, either by regular modulated datacommunication, or by constructing a respective code by means of whichsuch a probe may use more than one of these exclusively assigned slotsin order to determine its motion distance.

By arranging the allocated slots in an opposed order to the queue, i.e.,an order in which the increase in time corresponds to a decrease indistance from the mapping focus, (and thus the first assigned slot wouldbe allocated to the farthest position from the mapping focus in themapped road segment), and by using feedback to the probe which enablesto stop the process of sampling in any one mapping sample, it ispossible to save communication resources. The feedback message thatwould be transmitted to the probes would enable to stop the samplingprocess for a mapping sample when detecting the first probe (in theopposed queue) which by definition is farthest probe for the mappingsample. Furthermore, the opposite order of allocated slots could also beassigned in order to limit queue mapping to a minimum predeterminedrange of interest from the mapping focus, in order to save assigningslots for queues that are too short to be of interest. Any feedbackmessage, e.g. busy bits (used with DSMA) or other appropriate messageaccording to a predetermined protocol through the broadcast channel canbe used to stop further responses from probe in any mapping sample.

Further saving of communication resources with respect to slotallocation could preferably take benefit of allowing the possibility ofmissing the detection of a probe in a situation where it is expectedthat the probe, if it would be detected, would not have significanteffect on the determination of the length of the queue. For example, ifan a priori knowledge exists about the probe percentage amongst thearriving vehicles in a segment of road, then if for example theprobability of successive arrival of probes within a meaningful shorterdistance (shorter period of time), compared to the expectation, is notsufficiently high, then an allocation of slots to such a segment of roadwould preferably be saved. In such cases where there is low significanceof effect, rather than no significance, for the detection of probes,then the slots could be allocated for a shorter time, in order to savetime at the cost of lowering probability to detect a probe.

When allocation of adjacent frequency slots are assigned with respect todifferent areas it would preferably be worth to allocate such slots tothe respective areas so as to minimize the expected difference in radiopropagation path loss between the respective paths from these arearelated slots and a common base station. This would enable higherdiscrimination between signals that might be received with a very largedifference in received signal strength between each other, whileenabling the small signal to be detected.

1-4. (canceled)
 5. A method to adjust traffic prediction, the methodcomprising a plurality of iterations, wherein at least one iteration ofthe plurality of iterations comprises: providing by a device a trafficprediction update for route planning associated with a plurality ofvehicles, wherein said traffic prediction update is based, at least inpart, on route plan modifications resulting from give-up processesperformed in a prior iteration, wherein said give-up process comprisesmodification of a route plan, in accordance with a give-up criterion, toexclude from the route plan a congested route segment, which is expectedto have traffic congestion, and wherein said traffic prediction updateincludes a prediction, in a forward time interval, of congested trafficon a route segment that is still congested with respect to trafficpredicted on said route segment in said prior iteration; and in responseto the traffic prediction update, receiving, by said device, anindication that a vehicle of said vehicles has modified its route planfrom a planned route that includes said still congested route segment toa modified route that does not include said still congested routesegment.
 6. The method of claim 5, wherein the traffic prediction updateis further based on statistical traffic data.
 7. The method of claim 6including updating a statistical database according to the indication.8. The method of claim 5, wherein the give-up process comprises agive-up process utilizing one or more give-up criterion levels.
 9. Themethod of claim 8, wherein a give-up criterion level of the give-upcriterion levels includes a threshold to modify a route plan to a routehaving lower priority than the route plan of the prior iteration. 10.The method of claim 8, wherein a give-up criterion level of the give-upcriterion levels includes a threshold to modify a route plan to a routeplan having a higher travel time than the route plan of the prioriteration.
 11. The method of claim 8, wherein a give-up criterion levelof the give-up criterion levels includes a threshold to modify a routeplan to an earlier, more preferable, route.
 12. The method of claim 8,wherein a give-up criterion level of the give-up criterion levelsincludes a threshold to modify a route plan to a route having a-priorilower priority than the route plan of the prior iteration.
 13. Themethod of claim 5, wherein one or more of the iterations is based on oneor more random parameters.
 14. The method of claim 5, wherein thegive-up process in a successive iteration to a prior iteration modifiesplanned routes at a higher grade of give-up criterion level than theroute plan of the prior iteration.
 15. The method of claim 5, wherein alength of the modified route is greater than a length of the plannedroute.
 16. The method of claim 5 comprising: if the traffic predictionupdate indicates that too many mobile units gave up on said stillcongested route segment in the prior iteration, receiving one or moresignals from one or more of said vehicles, indicating that said one ormore vehicles modify their current routes to routes previously-used inthe previous iteration.
 17. The method of claim 16 comprising: receivinga signal indicating that at least one of said vehicles returns to apreviously-used route by re-making a prior give-up process with a lowergrade of give-up level criterion.
 18. The method of claim 16, whereinthe previously-used routes are shorter than the current routes.
 19. Themethod of claim 5 comprising: performing a plurality of iterations untilreaching a sufficient level of convergence.
 20. The method of claim 19comprising: performing said plurality of iterations by taking intoaccount a trade-off between low levels of give-up grades and high levelsof give-up grades.
 21. The method of claim 5 including broadcasting saidtraffic prediction update to the plurality of vehicles.
 22. The methodof claim 5, including, at a vehicle of the vehicles: receivingpositioning signals, planning a route according to traffic updates andaccording to a respective give-up process, transmitting radio signals toupdate a mapping system about route modification, and receiving radiosignals of traffic updates.
 23. The method of claim 5, wherein saidroute modification comprises modification to exclude from route plansone or more route segments affected by predicted traffic congestion. 24.A method of position related offering of an item, the method comprising:transmitting query information indicative of a price-related criterionrelating to a potential price of the item and a position-relatedcriterion; receiving one or more responses generated by one or moremobile units, wherein a response of a mobile unit is generated accordingto a match process between the price-related criterion and priceinformation related to said item as stored in said mobile unit, andwherein the response is based on a match between a position relatedcharacteristic of the mobile unit and the position-related criterion;based on said responses, estimating potential demand for the item; andtransmitting an offer to purchase the item based on said potentialdemand.